Why Small is Too Small a Term: Prevention Science for Health Disparities, Culturally Distinct Groups, and Community-Level Intervention.

Implications of the Advancing Small Sample Prevention Science Special Section are discussed. Efficiency and precision are inadequately considered in many current prevention-science methodological approaches. As a result, design and analytic practices pose difficulties for the study of contextual factors in prevention, which often involve small samples. Four primary conclusions can be drawn from the Special Section. First, contemporary statistical and measurement approaches provide a number of underutilized opportunities to maximize power. These strategies maximize efficiencies by optimizing design and resource allocation parameters, allowing for the detection of effects with small samples. Second, several alternative research designs provide both rigor and further optimize efficiencies through more complete use of available information, allowing study of important questions in prevention science for which only small samples may be accessible. Third, mixed methods hold promise for enhancing the utility of qualitative data in studies with small samples. Finally, Bayesian analytic approaches, through their use of prior information, allow for even greater efficiencies in work with small samples, and through their introduction in the routines of mainstream software packages, hold particular promise as an emergent methodology in prevention research.